Biology 代写:Build A Decision Support System Using Retinal Images
Background And Literature Search
Retinal images are widely used as tools to diagnose retinopathies by ophthalmologist. Retinal images are obtained using fundus camera. Figure 2.1 shows a fundus camera and figure 2.2 shows a normal human retinal obtained by fundus camera. A fundus camera or retinal camera is a specialized low power microscope with an inner attached camera designed to take photos for the interior surface of the eye . The interior surface of the eyes includes retina, optic disc, macula, and posterior pole.
Figure 2.1 Example of a fundus camera Figure 2.2 A normal retinal image.
The features that can found in majority of retinopathies retinal images are categorize into two groups which are bright spots and dark spots. Bright spots include optic disc, exudates and cotton wool spots while dark spots include blood vessels, haemorrhages and microaneurysms. By analyze these features, certain retinopathies can be detected. Examples of retinopathies that can be detected from retinal images are diabetic retinopathy, glaucoma, macular degeneration, hypertensive retinopathy and etc.
Figure 2.3 shows a retinal image for diabetic retinopathy. Most of the features are shown and labelled in this retinal image.
Cotton wool spots
Figure 2.3 Sample retinal images of diabetic retinopathy.
Features in Retinal Images for Disease Diagnosis
Exudates, cotton wool spots and optic disc are three types of bright spots in retinal images.
Exudates or hard exudates: visible as bright yellowish deposits with sharp margins on the retinal due to the leakage of blood from abnormal blood vessels. The weakened vessels walls causes out-pouching in their walls called microaneurysms, which may also leak. Exudates frequently arranged in circular pattern or crescents surrounding zones of retinal edema or group of microaneurysms. Besides, exudates also possible to arrange as individual dots, sheets, or confluent patches. Exudates represent accumulations of lipid and protein. If exudates encroach on the macula, vision will be affected .
Cotton wool spots: Cotton wool spots or soft exudates appear as white, pale yellow fluffy opaque area with ill-defined edges in retinal. They result from the damage of nerve fibers whereby the blood supply to that area has been impaired. The nerve fibers in that particular area are injured due to the absence of normal blood flow through the blood vessel there. Therefore, swelling will occur at that spot and it appear as cotton wool spots. Diseases such as diabetes and hypertension will affect the retinal and cause the occurrence of cotton wool spots .
Optic disc: Optic disc is the brightest part in the normal retinal image. It is pale, round or vertically oval disc. Normally, the disc is orange to yellowish-pink in colour with well defined margins. An optic disc is the entrance region of optic nerves and blood vessels to the retinal. It always acts as a landmark for other features in retinal image. In retinal image analysis, location of optic disc is important to measure distance and identify some anatomical parts in retinal images. The lack of light-sensitive cells, rods and cones at the optic disc results a physiological blind spot in the visual field of each eye. Glaucoma, a disease cause by degenerative optic nerve is basically related with a sustained increase of the eye pressure .
The dark spots in retinal can be dividing into three features which are blood vessels, haemorrhages, and microaneurysms.
Blood vessels: Blood vessels are the blood supply for retinal. Blood vessels appearance is an important indicator for many diagnoses such as diabetic retinopathy and hypertension. It can reflect different states of numbers of diseases, which also the pre-characteristic for the registration and mosaic of retinal images. Observable features of blood vessels such as diameter, colour, tortuosity (relative curvature), and opacity (reflectivity) can provide information on pathological changes caused by some diseases. The abnormalities of retinal blood vessels include blockages and bleeding (haemorrhages) from them.
Haemorrhages: Haemorrhages is the abnormal bleeding of the damaged blood vessels in retinal. The appearance of haemorrhages may have many kinds of shapes sometimes resembling bundles of straw but they also can be round or flame shaped . The bleeding of vessels which are haemorrhages can cause temporary or permanent loss of visual accuracy. There is various cause of haemorrhage which major causes are diseases such as diabetic retinopathy, hypertension and prematurity retinopathy. Besides, it can also caused by shaking, particularly in young infants.
Microaneurysms: Microaneurysms are included in dark spots in retinal image that appear as small dark reddish dots on retinal surface. Its definition is less than the diameter of the major optic veins as they cross the optic disc. Microaneurysms are small out pouching in capillary vessels. Normally, capillary vessels are not visible in retinal image. Due to the increasing number of microaneurysms, these small dots appear between the visible retinal vasculature. Microaneurysms are caused by weakening of vessels wall or diseases include diabetic retinopathy.
Nowadays, there is an increasing interest for creating system and algorithms that can support for screen a big amount of patients for sight threatening diseases such as diabetic retinopathy, glaucoma, and hypertension retinopathy. These systems and algorithms provide automated detection of these retinopathies. Retinal images are widely used as tools to screen and diagnose retinopathies by ophthalmologist. Currently, digital image processing is very famous and practical for retinopathies diagnosis. By using image processing, features such as blood vessels and exudates can be detected, extracted, and analyzed for the purpose of diagnosis. In the literature, there are some examples of image processing techniques which have been applied in identification and detection of features in retinal image for disease diagnosis. Research is done on several literatures about features detection and extraction in retinal images.